Differences in immune indicators among normal, high-risk, and esophageal cancer populations and development of a predictive model.

Objective: To systematically compare the differences in immune characteristics among three populations (normal group, high-risk group, and esophageal cancer [EC] patients group) and construct a predictive model based on immune metrics, thereby exploring its value in EC risk assessment and early diagnosis. Methods: A total of 440 participants were enrolled, including 173 normal individuals, 162 high-risk individuals, and 105 EC patients. Peripheral blood samples were collected, and 75 immune metrics were detected using flow cytometry. First, 21 highly correlated indicators were eliminated through the Pearson correlation matrix (|r|≥0.7), leaving 54 indicators for further analysis. Multivariate analysis of variance (MANOVA) was used to assess the effect of EC grouping on immune metrics, adjusting for gender and age. Significant indicators were analyzed using analysis of covariance (ANCOVA) and the false discovery rate (FDR). Stratified sensitivity analysis was conducted to verify the robustness of the results. Finally, a predictive model was built using 12 key immune metrics, and 10 covariates. The model performance was evaluated by 10-fold cross-validation, neural network algorithm, accuracy, Kappa value, and area under the receiver operating characteristic curve (AUC). Results: MANOVA showed that EC grouping had a significant overall impact on the 54-immune metrics (Pillai = 0.585, Conclusion: This study demonstrates distinct and stable immune profiles across EC risk groups. The immune-based machine learning model effectively differentiates normal, high-risk, and cancer populations, offering a promising non-invasive tool for early risk assessment and screening of esophageal cancer.